Deep Representation Learning for Vietnamese Speaker Recognition

Abstract

Speaker recognition is the process of identifying an individual from their voices, and it has been widely applied in many real-world applications. Recently, deep learning has instigated a revolutionary high success rate in speaker recognition. The major advantage of deep learning over conventional methods for speaker recognition is attributed to its representation ability, and the ability to produce highly abstract embedding features from utterances. Recent researches had revealed that deep learning method in learning speaker features from raw data, is strongly depending on a speaker's language. However, only minimal researches had done on deep learning over Vietnamese speaker recognition to present. Nevertheless, this paper has proposed a deep transfer learning method which integrates both transfer learning and deep learning to build models for Vietnamese speaker recognition. Our experimental results indicated that the proposed method is able to build accurate models for Vietnamese speaker recognition.